Information concerning the electrical activities of the organ systems is widely used to assess the health characteristics of both people and animals. For example, analysis of the morphology of the waveforms of the electrocardiogram provides an indication of the current status of cardiac electrical activity. The standard clinical method of determining cardiac electrical activity is the 12-lead electrocardiograms or vectorcardiograms, which are obtained by placing a few electrodes on the limbs and chest to record the time courses of the electrical potential on these electrodes. Similarly, 10-20 electroencephalograms are widely used in the clinical setting to help diagnose brain abnormalities.
Recently, techniques have been introduced in order to provide the spatial information on bioelectrical activities by mapping the electrical potential over a large area of the body surface. These methods, in general, use a large number of electrodes placed over a large area over the body surface, thereby providing a high spatial sampling over the space domain. Unfortunately, it has been shown that the direct display of a complete set of distribution of bioelectrical potentials over the body surface still does not provide spatial details on the underlying bioelectrical activities, because of distortion introduced by the body volume conductor.
Another class of instrumentation is available for the specific purpose of determining origins of electrophysiological or pathophysiological events using electrical methods. This technology also uses electrodes, but placing them directly close to or on the surface of the organs. Therefore this technology is invasive. Although these methods provide much higher spatial details in determining origins of bioelectrical activities, the invasive nature of this technology limits the application to human beings.
Thus, there is a great need for non-invasive analytical instruments and methods that would provide important spatial information regarding the bioelectrical activities.
Attempts have been made to process the recorded body surface potentials or to correct the volume conduction distortion by incorporating the properties of passive body conductor. The surface Laplacian over the body surface has been used to improve the spatial resolution of bioelectrical signal analysis. It has been shown that the surface Laplacian distribution facilities the analysis and interpretation of biosignals. He and Cohen introduced a technique to measure and visualize the body surface Laplacian using a set of bipolar electrodes, which was published in IEEE Transactions on Biomedical Engineering, vol. 39, 1179, 1992. See also U.S. Pat. No. 5,146,926. Oostendorp and van Oosterom gave a numerical algorithm on evaluating the surface Laplacian from assumed bioelectrical sources, see "The surface Laplacian of the potential: Theory and applications," IEEE Trans. On Biomedical Engineering, vol. 43, 394, 1996. An approach has also been described by He to estimate the body surface Laplacian electrograms from potentials, see "Principles and Applications of the Laplacian Electrocardiogram," IEEE Engineering in Medicine and Biology, 133, 1997. Nunez et al. studied the surface Laplacian on the scalp using a spline algorithm, see "A theoretical and experimental study of high resolution EEG based on surface Laplacian and cortical imaging", Electroenceph. and Clin. Neurophysiol. 90, 40, 1994. Gevins et al. described a method of estimating surface Laplacian in a realistically shaped head model from scalp potentials, see U.S. Pat. No. 5,331,970. In these approaches, the surface Laplacian of the potential was measured or estimated from potential data using a local or global estimation schemes. However, in the prior art, no descriptions have been given on using the spatial pre-filtering before the estimation of the surface Laplacian of the potential. No descriptions have been given to conduct spatial threshold filtering using both potential and the surface differentials of the potential in spatial analysis of bioelectrical activity.
Attempts have also been made to reduce the volume conduction distortion by estimating the bioelectrical potentials over the brain surface from body surface potentials. Srebro et al. linked the evoked potential field on the scalp with brain surface field by assuming the head being homogeneous, see "Estimating regional brain activity from evoked potential field on the scalp", published by R. Srebro et al. in IEEE Trans. Biomed. Eng. 40, 509, 1993. Regularized inversion is applied to obtain the brain surface potential estimation. However, the lack of the significant inhomogeneity (the skull) in their head model results in considerable numerical errors. He et al. proposed an improved version of cortical imaging algorithm by incorporating a 3-spheres inhomogeneous head model and a closed spherical dipole layer of up to 1000 dipoles. See "Cortical source imaging from scalp electroencephalograms", published in Med. Biol. Eng. Comput. Vol. 34, 257, 1996. Recently, Babiloni et al. further extended the cortical imaging algorithm to a realistically shaped inhomogeneous head model with 364 dipoles. The boundary element technique was used to evaluate the potential field in the realistically shaped head model in Babiloni's algorithm, although the brain sources are still assumed to consist of 364 dipoles inside the brain. See "High resolution EEG: a new model-dependent spatial deblurring method using a realistically-shaped MR-constructed subject's head model", published by F. Babiloni et al. in Electroencephalography and clinical Neurophysiology, vol. 102, 69, 1997. Gevins et al. reported a cortical imaging technique in a realistically shaped inhomogeneous head model using finite element method, see "High Resolution EEG: 124-channel recording, spatial deblurring and MRI integration methods," published in Electroenceph. clin. Neurophy., vol. 90, 337, 1994. See also U.S. Pat. No. 5,331,970. In this method, Poisson's equation is applied to a conducting volume between scalp and cortical surface, and the finite element method is used to handle the complex geometry and varying conductivity of the head. However, the finite element method based technique has intrinsic limitations because it requires detailed three-dimension information on the tissue conductivity, which has only been known approximately up to date. The need to create and manipulate a large amount of three-dimension information requires significant storage and computation capability. Furthermore, the reported finite element method inverse reconstruction procedure necessitates the solution of a nonlinear problem. Therefore, there is a great need to further improve brain electric imaging technique.